YUSUF

🇬🇭 Biochemist ➡️ Data Scientist | PhD in Biomedical Data Science (Child Health) | Early Career Researcher | Gaming when not coding 🎮 | #TechEnthusiast

[YUSUF @ay_omide]

@thebankofghana @enbeghan

[Ismini Lourentzou @Ismini_L]

🚀 Call for Papers & Challenge: CV4CHL @CVPR 2026 in Denver! 🏔️
We’re bridging the gap in Computer Vision for children’s health, education & development. Join us! 👶💻

🏆 Children's Gait Competition: Win an NVIDIA RTX 5080 GPU or Latest AR Glasses! 🎮🔥

📅 Deadlines (AoE):
• Proceeding Papers: Mar 10
• Non-Proceeding: May 10
• Challenge Submission: May 10

🔗 Details & Submission: https://t.co/sMQLiSAhas
+ A fantastic keynote speaker lineup! 🤩
#CVPR2026 #ComputerVision #AIforGood #NVIDIA #RTX5080 #MedAI #CV4CHL

[liz tan @liztansz]

Say you have trained your deep learning model. It works. But do you know what it has actually learned?

🚀 We’ve built SymTorch: a library that translates deep learning models into human-readable equations.

I've attached here a quick video demonstrating how SymTorch works.

[Science News @SciencNews]

AI for Grant Writing

[Shraddha Bharuka @BharukaShraddha]

Stop wasting hours trying to learn AI. 📘📚

I have already done it for you.

With one list. Zero confusion. And no fluff

📹 Videos:

1. LLM Introduction: https://t.co/kyDon6qLrb

2. LLMs from Scratch: https://t.co/2hyMhuKoiI

3. Agentic AI Overview (Stanford): https://t.co/FXu6cAqITC

4. Building and Evaluating Agents: https://t.co/ZigR1tdOFL

5. Building Effective Agents: https://t.co/uYwfwO55mO

6. Building Agents with MCP: https://t.co/4arFTW1b3i

7. Building an Agent from Scratch: https://t.co/eOmveyM9Hz

8. Philo Agents: https://t.co/zLu7x1tx9m

🗂️ Repos

1. GenAI Agents: https://t.co/eXCl2YaRPv

2. Microsoft's AI Agents for Beginners: https://t.co/3CSW4zPAwf

3. Prompt Engineering Guide: https://t.co/GVzvxPYDVO

4. Hands-On Large Language Models: https://t.co/0rgDvhx3pI

5. AI Agents for Beginners: https://t.co/3CSW4zPAwf

6. GenAI Agentshttps://lnkd.in/dEt72MEy

7. Made with ML: https://t.co/9z5KHF9DMe

8. Hands-On AI Engineering:https://t.co/dldAj5Xkr6

9. Awesome Generative AI Guide: https://t.co/U2WZhT4ERV

10. Designing Machine Learning Systems: https://t.co/sYAZX34YdQ

11. Machine Learning for Beginners from Microsoft: https://t.co/NjFxHbC9jZ

12. LLM Course: https://t.co/N34YTPu1OK

🗺️ Guides

1. Google's Agent Whitepaper: https://t.co/bW3Ov3vMW0

2. Google's Agent Companion: https://t.co/wredwWAbBA

3. Building Effective Agents by Anthropic: https://t.co/fxtE4alVrJ.

4. Claude Code Best Agentic Coding practices: https://t.co/lLSwJ9pG7C

5. OpenAI's Practical Guide to Building Agents: https://t.co/xgkEIogGfh

📚Books:

1. Understanding Deep Learning: https://t.co/CjcKpTemmV

2. Building an LLM from Scratch: https://t.co/DaWBxOx8o3

3. The LLM Engineering Handbook: https://t.co/ZA1n0N41Mf

4. AI Agents: The Definitive Guide - Nicole Koenigstein: https://t.co/boLkl1VlKb

5. Building Applications with AI Agents - Michael Albada: https://t.co/H1Xf5EkJLL

6. AI Agents with MCP - Kyle Stratis: https://t.co/JI3ELQZE6a

7. AI Engineering: https://t.co/Xk0JzMIf7o

📜 Papers

1. ReAct: https://t.co/QNqE4UU55w

2. Generative Agents: https://t.co/CwEpoJgY1U.

3. Toolformer: https://t.co/5m9xZd5teZ

4. Chain-of-Thought Prompting: https://t.co/KjVlgdWi77.

🧑🏫 Courses:

1. HuggingFace's Agent Course: https://t.co/7FSUYKxIdG

2. MCP with Anthropic: https://t.co/IkZGiWm2yS

3. Building Vector Databases with Pinecone: https://t.co/2YRoMfLdXd

4. Vector Databases from Embeddings to Apps: https://t.co/23A50ixbHJ

5. Agent Memory: https://t.co/uc3L9BrNF7

Repost for your network ♻️

[Dan Kornas @DanKornas]

UC Berkeley offers two free courses on LLM agents, one at the foundational level and one at the advanced level, taught by leading researchers and practitioners from DeepMind, Meta, and top universities.

Together, they cover essentially everything you need to understand and build agents, drawing from some of the best resources available today.

[Kshitij Mishra | AI & Tech @DAIEvolutionHub]

10 GitHub repos that will level up your AI Agent skills (SAVE THIS)🔖

1. Hands-On Large Language Models
Complete code notebooks from basics to advanced fine-tuning.
🔗 https://t.co/PlJx0YuSvj

2. AI Agents for Beginners
A free 11-part intro course to build your first agents.
🔗 https://t.co/58HpG0Kt3F

3. GenAI Agents
Tutorials and code for building generative AI agents.
🔗 https://t.co/2IMMmN5qVw

4. Made with ML
Learn to design, build, and deploy real ML apps.
🔗 https://t.co/fZ3VS9k1Qt

5. Prompt Engineering Guide
Learn to write powerful and effective prompts.
🔗 https://t.co/U7l7jeSY01

6. Hands-On AI Engineering
Practical LLM-powered apps and agent examples.
🔗 https://t.co/6oGigYotUj

7. Awesome Generative AI Guide
Curated hub for genAI research and tools.
🔗 https://t.co/w586KFLh9u

8. Designing Machine Learning Systems
Summaries and resources from the popular ML systems book.
🔗 https://t.co/V5cHFb1m1s

9. ML for Beginners (Microsoft)
Free beginner-friendly ML curriculum.
🔗 https://t.co/KcFtbyK6Ky

10. LLM Course
Roadmaps and hands-on notebooks to build LLM apps.
🔗 https://t.co/wqL0IxhFMe

I'm curating 50+ AI Agent resources on my profile worth checking out 👋

[Vaishnavi @_vmlops]

Harvard released a Free Book on ML system engineering
https://t.co/GmEqhiImjR

[Swapna Kumar Panda @swapnakpanda]

Machine Learning from Stanford- by Andrew Ng

227-pages lecture notes for the course CS229 is now AVAILABLE.

Reply if you want to get a copy.

[Shraddha Bharuka @BharukaShraddha]

Stop wasting hours trying to learn AI. 📘📚

I have already done it for you.

With one list. Zero confusion. And no fluff

📹 Videos:

1. LLM Introduction: https://t.co/kyDon6qLrb

2. LLMs from Scratch: https://t.co/2hyMhuKoiI

3. Agentic AI Overview (Stanford): https://t.co/FXu6cAqITC

4. Building and Evaluating Agents: https://t.co/ZigR1tdOFL

5. Building Effective Agents: https://t.co/uYwfwO55mO

6. Building Agents with MCP: https://t.co/4arFTW1b3i

7. Building an Agent from Scratch: https://t.co/eOmveyM9Hz

8. Philo Agents: https://t.co/zLu7x1tx9m

🗂️ Repos

1. GenAI Agents: https://t.co/eXCl2YaRPv

2. Microsoft's AI Agents for Beginners: https://t.co/3CSW4zPAwf

3. Prompt Engineering Guide: https://t.co/GVzvxPYDVO

4. Hands-On Large Language Models: https://t.co/0rgDvhx3pI

5. AI Agents for Beginners: https://t.co/3CSW4zPAwf

6. GenAI Agentshttps://lnkd.in/dEt72MEy

7. Made with ML: https://t.co/9z5KHF9DMe

8. Hands-On AI Engineering:https://t.co/dldAj5Xkr6

9. Awesome Generative AI Guide: https://t.co/U2WZhT4ERV

10. Designing Machine Learning Systems: https://t.co/sYAZX34YdQ

11. Machine Learning for Beginners from Microsoft: https://t.co/NjFxHbC9jZ

12. LLM Course: https://t.co/N34YTPu1OK

🗺️ Guides

1. Google's Agent Whitepaper: https://t.co/bW3Ov3vMW0

2. Google's Agent Companion: https://t.co/wredwWAbBA

3. Building Effective Agents by Anthropic: https://t.co/fxtE4alVrJ.

4. Claude Code Best Agentic Coding practices: https://t.co/lLSwJ9pG7C

5. OpenAI's Practical Guide to Building Agents: https://t.co/xgkEIogGfh

📚Books:

1. Understanding Deep Learning: https://t.co/CjcKpTemmV

2. Building an LLM from Scratch: https://t.co/DaWBxOx8o3

3. The LLM Engineering Handbook: https://t.co/ZA1n0N41Mf

4. AI Agents: The Definitive Guide - Nicole Koenigstein: https://t.co/boLkl1VlKb

5. Building Applications with AI Agents - Michael Albada: https://t.co/H1Xf5EkJLL

6. AI Agents with MCP - Kyle Stratis: https://t.co/JI3ELQZE6a

7. AI Engineering: https://t.co/Xk0JzMIf7o

📜 Papers

1. ReAct: https://t.co/QNqE4UU55w

2. Generative Agents: https://t.co/CwEpoJgY1U.

3. Toolformer: https://t.co/5m9xZd5teZ

4. Chain-of-Thought Prompting: https://t.co/KjVlgdWi77.

🧑🏫 Courses:

1. HuggingFace's Agent Course: https://t.co/7FSUYKxIdG

2. MCP with Anthropic: https://t.co/IkZGiWm2yS

3. Building Vector Databases with Pinecone: https://t.co/2YRoMfLdXd

4. Vector Databases from Embeddings to Apps: https://t.co/23A50ixbHJ

5. Agent Memory: https://t.co/uc3L9BrNF7

Repost for your network ♻️

[MTN Ghana @MTNGhana]

The hustle will pay off, just keep pushing. Make the move today, pursue the dream.
Make We Do Am!

[Krishna Agrawal @Krishnasagrawal]

Stop wasting hours trying to learn AI. 📘📚

I have already done it for you.

With one list. Zero confusion. And no fluff

📹 Videos:

1. LLM Introduction: https://t.co/Avq3pNZxWY

2. LLMs from Scratch: https://t.co/nGJGCQYi89

3. Agentic AI Overview (Stanford): https://t.co/1JbA2JypnJ

4. Building and Evaluating Agents: https://t.co/02o8b7RAS2

5. Building Effective Agents: https://t.co/Jw0cd23A3K

6. Building Agents with MCP: https://t.co/9uvcpin1j9

7. Building an Agent from Scratch: https://t.co/QiYCLK48tn

8. Philo Agents: https://t.co/TCSX4hnKRf

🗂️ Repos

1. GenAI Agents: https://t.co/VdfLWAA3wW

2. Microsoft's AI Agents for Beginners: https://t.co/3SpFdnMcQa

3. Prompt Engineering Guide: https://t.co/4W7Eh6NKaE

4. Hands-On Large Language Models: https://t.co/LEtKGYBdgU

5. AI Agents for Beginners: https://t.co/3SpFdnMcQa

6. GenAI Agentshttps://lnkd.in/dEt72MEy

7. Made with ML: https://t.co/sL3gbmGUky

8. Hands-On AI Engineering:https://t.co/J79go1Ivlo

9. Awesome Generative AI Guide: https://t.co/xXF08rY8zP

10. Designing Machine Learning Systems: https://t.co/Q39XZLn50b

11. Machine Learning for Beginners from Microsoft: https://t.co/vfkyHL1Gnx

12. LLM Course: https://t.co/wuDkhQ0572

🗺️ Guides

1. Google's Agent Whitepaper: https://t.co/BSjthrG81u

2. Google's Agent Companion: https://t.co/wdzh5zuSvp

3. Building Effective Agents by Anthropic: https://t.co/HmYVgMIA7l.

4. Claude Code Best Agentic Coding practices: https://t.co/H3JpJWlpp3

5. OpenAI's Practical Guide to Building Agents: https://t.co/G1TL1Z2TR3

📚Books:

1. Understanding Deep Learning: https://t.co/EEqkGL7lHe

2. Building an LLM from Scratch: https://t.co/8Ehn91NNxE

3. The LLM Engineering Handbook: https://t.co/yzvXeQAgtV

4. AI Agents: The Definitive Guide - Nicole Koenigstein: https://t.co/FKEzpBewe4

5. Building Applications with AI Agents - Michael Albada: https://t.co/nvBaoR6FxZ

6. AI Agents with MCP - Kyle Stratis: https://t.co/u0M1GfBlsL

7. AI Engineering: https://t.co/A2QxhrpmDc

📜 Papers

1. ReAct: https://t.co/Vbc3vWxQHO

2. Generative Agents: https://t.co/PvNHYnqsOD.

3. Toolformer: https://t.co/HFdds582DI

4. Chain-of-Thought Prompting: https://t.co/GxX5BxXLzV.

🧑🏫 Courses:

1. HuggingFace's Agent Course: https://t.co/zhizqjunzS

2. MCP with Anthropic: https://t.co/LqDZ0oCRfj

3. Building Vector Databases with Pinecone: https://t.co/ncqSVaVHqH

4. Vector Databases from Embeddings to Apps: https://t.co/JOl7SIlguq

5. Agent Memory: https://t.co/5m0UrGwDM9

Repost for your network ♻️
&follow for more stuff on building AI Agents.

[Tom Dörr @tom_doerr]

Course material for learning AI engineering

https://t.co/7cGLSG5123

[Pau Labarta Bajo @paulabartabajo_]

RL is back, baby! Here's a course to get you started

𝗖𝗼𝗻𝘁𝗲𝘅𝘁

Reinforcement Learning (RL) is one of the key ingredients to post-train Language Models for long, complex tasks, hard-for-humans tasks, especially with verifiable rewards. For example, maths or coding, which is what DeepSeekMath excels at.

RL is no new kid in the block. RL was born in the 1980s, and over time merged with other very important branches in the history of AI, like Deep Learning. This is what we nowadays call Deep RL.

@GoogleDeepMind and @OpenAI did heavy research in Deep RL in the context of game environments like Atari games and board games, creating mega-popular models like AlphaGo and its next evolution AlphaZero, who mastered the most popular heavy-reasoning board games (chess, shogi, go) by playing against itself.

𝗪𝗵𝘆 𝗶𝘀 𝘁𝗵𝗶𝘀 𝗿𝗲𝗹𝗲𝘃𝗮𝗻𝘁 𝗶𝗻 𝘁𝗼𝗱𝗮𝘆'𝘀 𝗔𝗜 𝘄𝗼𝗿𝗹𝗱?

Language Models generate responses token by token, receiving a reward at the end of their text completion. If you frame this as a RL (why not?) you can start using all the powerful algorithms that DeepMind, OpenAI and other researchers developed 10 years ago.

The fundamentals of RL have not changed an inch. What has changed are the RL algorithms used for LMs (like GRPO) which are slight modifications of existing algos developed almost 10 years ago (like PPO).

If you master the fundamentals, the path ahead is clear.

𝗪𝗵𝗮𝘁'𝘀 𝗻𝗲𝘅𝘁?

I am preparing a hands-on tutorial on GRPO for post-training a VLM to do browser automation in collaboration with @huggingface .

It will be fun, but I want you to be prepared. Hence, I am sharing with you a previous hands-on course I created, that teaches the fundamentals of RL.

This is the first open-source I created years ago.
My older son :-)

Enjoy ↓
https://t.co/pS2a3Fd6zf

[Google Cloud Tech @GoogleCloudTech]

The 5-Day AI Agents Intensive course with @kaggle is live! Learn about autonomous AI agents and create your first systems using the Agent Development Kit (ADK), powered by Gemini.

Plus, check out our new Introduction to Agents whitepaper → https://t.co/MGhDGplrbr

[ℏεsam @Hesamation]

Google just dropped a new 50-page doc on building agents that actually work in the real world. it's a fast introduction to the theory of what you must know about agents.

It covers:
→ core agent architecture
→ LLM (the brain behind agents)
→ tools (the hands of the agents)
→ multi-agent orchestration
→ how to deploy agents
→ evaluation and metrics
→ self-evolving learning agents
→ how agents evolve and learn
→ covering AlphaEvolve example

you can download and read it from Kaggle: https://t.co/nLKRlX0RZs

[YUSUF @ay_omide]

@B_Ishaque07 Chale

[Rahul Roy @Ai_here202]

This "AI Engineering" Book is Goat

But here’s the secret:
Its GitHub repo has a goldmine

Learn LLM apps, prompt engineering, fine-tuning, RAG & more.

To Get it:

1. Follow me (so I can DM you)
2. Repost
3. Comment "AI"

[Julian Goldie SEO @JulianGoldieSEO]

FREE Claude 4.5 Course: Build Like an AI GENIUS! 🔥

Want the full guide? DM me.

[Julian Goldie SEO @JulianGoldieSEO]

AI just became your coding sidekick.

VS Code now runs GPT5 inside.

  • Install latest VS Code first.
  • Add the free AI Toolkit extension.
  • Connect GPT5, Claude, or local models.
  • Enable agent mode for full automation.
  • Watch it refactor, test, and commit code.

Save this video, you’ll code ten times faster.

Want the SOP? DM me. 💬